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  {
   "source": [
    "## 任务：\n",
    "基于generated_data.csv数据，建立线性回归模型，预测 x=3.5 对应的y值，评估模型表现"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('generated_data.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "   x   y\n0  1   7\n1  2   9\n2  3  11\n3  4  13\n4  5  15",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>x</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>13</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>15</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 46
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "0     1\n1     2\n2     3\n3     4\n4     5\n5     6\n6     7\n7     8\n8     9\n9    10\nName: x, dtype: int64 0     7\n1     9\n2    11\n3    13\n4    15\n5    17\n6    19\n7    21\n8    23\n9    25\nName: y, dtype: int64\n"
    }
   ],
   "source": [
    " # 赋值\n",
    " x = data.loc[:, 'x']\n",
    " y = data.loc[:, 'y'] \n",
    " print(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 展示图形\n",
    "from matplotlib import pyplot as plt\n",
    "plt.figure(figsize=(10, 10))\n",
    "plt.scatter(x, y)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 建立线性回归模型\n",
    "from sklearn.linear_model import LinearRegression\n",
    "lr_model = LinearRegression()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x = np.array(x).reshape(-1, 1)\n",
    "y = np.array(y).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "LinearRegression()"
     },
     "metadata": {},
     "execution_count": 51
    }
   ],
   "source": [
    "lr_model.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[[ 7.]\n [ 9.]\n [11.]\n [13.]\n [15.]\n [17.]\n [19.]\n [21.]\n [23.]\n [25.]]\n[[ 7]\n [ 9]\n [11]\n [13]\n [15]\n [17]\n [19]\n [21]\n [23]\n [25]]\n"
    }
   ],
   "source": [
    "# 预测值\n",
    "y_predict = lr_model.predict(x)\n",
    "print(y_predict)\n",
    "# 实际值\n",
    "print(y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[[12.]]\n"
    }
   ],
   "source": [
    "# 预测 x=3.5 对应的y值\n",
    "y_test = lr_model.predict([[3.5]])\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[[2.]] [5.]\n"
    }
   ],
   "source": [
    "# a  b  打印\n",
    "a = lr_model.coef_\n",
    "b = lr_model.intercept_\n",
    "print(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "1.4909471108677122e-29 1.0\n"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "MSE = mean_squared_error(y, y_predict)\n",
    "R2 = r2_score(y, y_predict)\n",
    "print(MSE, R2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(y, y_predict)\n",
    "plt.show()"
   ]
  },
  {
   "source": [
    "## MSE(均方误差)越小越好，R2分数越接近1越好，y<sup>'</sup> vs y集中度越高越好（越接近直线分布）"
   ],
   "cell_type": "markdown",
   "metadata": {}
  }
 ]
}